Foundations of Computational Intelligence: Volume 4: Bio-Inspired Data Mining / Edition 1by Ajith Abraham
Computing techniques inspired by biological elements such as nervous systems, immune systems and genetics have been used in data mining. This book, one of a series on the foundations of Computational Intelligence, is focused on bio-inspired data mining.See more details below
Computing techniques inspired by biological elements such as nervous systems, immune systems and genetics have been used in data mining. This book, one of a series on the foundations of Computational Intelligence, is focused on bio-inspired data mining.
Table of Contents
Part-I: Bio-inspired approaches in sequence and data streams .- Adaptive and Self-adaptive Techniques for Evolutionary Forecasting Applications Set in Dynamic and Uncertain Environments .- Sequence Pattern Mining: Genetic Network Programming Approach.- Growing Self-Organizing Map for Online Continuous Clustering.- Synthesis of Spatio-Temporal Models by the Evolution of Non-Uniform Cellular Automata.- Part-II Bio-inspired approaches in classification problem.- Genetic Selection Algorithm and Cloning for Data Mining with GMDH Method .- Inducing Relational Fuzzy Classification Rules by means of Cooperative Coevolution.- Post-processing Evolved Decision Trees .- Part-III: Evolutionary Fuzzy and Swarm in Clustering Problems.- Evolutionary Fuzzy Clustering: An Overview and Efficiency Issues.- Stability-based Model Order Selection for Clustering Using Multiple Cooperative Swarms .- Data-mining protein structure by clustering, segmentation and evolutionary algorithms .- A clustering genetic algorithm for genomic data mining.- Detection of Remote Protein Homologs using Social Programming.- Part-V: Bio-inspired approaches in information retrieval and visualization.- Optimizing Information Retrieval Using Evolutionary Algorithms and Fuzzy Inference System .- Web data clustering .- Efficient Construction of Image Feature Extraction Programs by Using Linear Genetic Programming with Fitness Retrieval and Intermediate-result Caching.- Mining Network Traffic Data for Attacks through MOVICAB-IDS.
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